Value assignment for customizable quality controls

ABSTRACT

Methods are provided for end users of diagnostic measurement procedures to prepare quality controls having desired analyte recoveries, estimate recoveries of quality controls already prepared, and compare estimated and measured recoveries. To prepare a quality control containing a particular analyte, a desired recovery of a measurement procedure for the analyte can be scaled by a correlation factor to obtain a target nominal concentration of the analyte in the quality control. Alternatively, the nominal concentration of an analyte in a quality control can be scaled by a correlation factor to obtain a predicted recovery of a measurement procedure for the analyte. The correlation factors can be based on recovery data previously obtained using the measurement procedure and optionally one or more reference procedures, and can be calculated using regression analysis of these data. Each quality control can be prepared by dissolving a number of solid beads containing the analyte(s) of interest in a volume of base matrix.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

The present patent application is a divisional application of U.S.patent application Ser. No. 14/831,765, filed on Aug. 20, 2015, now U.S.Pat. No. 9,885,728, which claims benefit of priority to U.S. ProvisionalPatent Application No. 62/041,398, filed on Aug. 25, 2014, both of whichare herein incorporated by reference in their entirety for all purposes.

BACKGROUND OF THE INVENTION

Clinical diagnostics laboratories help healthcare professionalsworldwide monitor the health and disease states of patients. Theselaboratories employ procedures to measure the concentration of one ormore analytes, such as salts, sugars, proteins, hormones, or dissolvedgasses, in a sample of tissue or bodily fluid obtained from anindividual patient. The measured concentration of an analyte is comparedwith a threshold or range that distinguishes between normal and abnormalconcentrations for the patient, with respect to the population to whichthe patient belongs. Based upon the comparison, additional tests can beprompted or a diagnosis for the patient can be made.

To ensure that analytical procedures are used to make accuratediagnoses, these procedures are calibrated with calibrators containingknown and/or reproducible concentrations of certain analytes. Forexample, several calibrators containing different levels of an analytecan be used to construct a response curve of a particular instrument forthat analyte. The concentration of the analyte in a patient sample canthen be determined on the instrument by measuring the sample andinterpolating along the response curve. Quality controls can also beused to test whether analyte concentrations reported by an instrumentare consistent over time. An instrument can be tested periodically(e.g., daily or weekly) with a control and the historical distributionof reported analyte concentrations can be examined, for example usingLevey-Jennings charts or Westgard rules. If a control measurementdeviates significantly from recent measurements or the historical mean,then measurements of patient samples can be discontinued until theinstrument can be serviced or recalibrated. Control measurements can beshared among laboratories to ensure that different instruments of thesame design provide consistent results when used to detect the sameanalyte.

Commercially available quality controls are typically prepared byspiking one or more analytes into a base matrix containing variousadditives such as stabilizers and antimicrobial agents. Base matricescan be manufactured from processed human bodily fluids, such as urine orserum, to ensure similarity between the quality controls and patientsamples. When multiple analytes are present, they can be for relatedmedical conditions (for example, markers for different tumors) ordetectable by the same method (for example, photometry). Qualitycontrols are typically offered in bi-level or tri-level configurationsto monitor and challenge the performance of a measurement procedure atanalyte concentrations above, near, and/or below a decision threshold.Quality control materials are designed to be stable and cost-effective,and should provide lot-to-lot reproducibility for analyte test results.

Once a quality control for an analyte is prepared, a ‘recovery’ orreported concentration for the analyte can be determined by the end useror control manufacturer. The recovery is particular to the instrument ormeasurement procedure to which the control is applied, and can be statedas a mean or range. In the case of ‘unassayed’ controls, the end userdetermines recoveries using his or her own laboratory procedures ornationally or internationally recognized protocols, such as Clinical andLaboratories Standards Institute document C24-A33. Such protocols caninvolve testing the control on an instrument repeatedly over a shortperiod of time (for example, 20 data points over two to three weeks) andcomputing the mean and standard deviation of recoveries measured duringthis time. For ‘assayed’ controls, the manufacturer provides expectedmeans and ranges of recoveries for all analytes included in the controlfor one or more procedures. For this purpose, the control manufacturertests a sufficiently large sample of each product lot with themeasurement procedures to establish a statistically valid mean andstandard deviation for the analyte at each level provided (FDA GuidanceDocument—Points to Consider Document on Assayed and Unassayed QualityControl Material, Draft Release Feb. 3, 1999). End users can alsoestablish means and ranges for recoveries of assayed controls based ontheir own protocols prior to using the controls to monitor theperformance of their test methods.

When a quality control is tested using multiple clinical diagnosticmeasurement procedures, the procedures can yield different recoveriesfor the same analyte. The differences can be due to differences or lackof standardization in assay architectures, detection technologies, orvarious parameters of the procedures. These differences can be difficultto reconcile, especially when no absolute standards for the analyteexist to provide well-known or ‘true’ concentrations. As a result,separate recovery ranges or thresholds must be established for eachmeasurement procedure to identify normal and abnormal concentrations ofthe analyte in patient samples. The task of establishing theserecoveries can be made more difficult when the concentrations of ananalyte that are considered normal or abnormal vary. For example, onepatient can be expected to have a higher concentration of an analyte inhis or her bodily fluid than another patient, due to differences in thepatients' ages, weights, ethnicities, general physiological states, orother factors. Thus, to make consistent diagnoses for many patientsusing multiple measurement procedures, recoveries of these proceduresfor the analyte often must be established at more than just two or threeanalyte levels.

Different diagnostic procedures often have different measurement rangesfor a given analyte, and exhibit other differences in performance interms of precision, accuracy, limits of quantitation (LOQ), limits ofdetection (LOD), or linearity. As a result, the procedures may not beamenable to monitoring with a common set of pre-prepared qualitycontrols. Commercially available controls, for example those from RandoxLaboratories Ltd. and Thermo Scientific, may not provide an analyte ofinterest in the concentrations needed to monitor all widely useddiagnostic procedures for that analyte. Many instrument manufacturersprovide their own quality controls for analytes to which theirinstruments are sensitive. But the controls for one instrument may notbe usable on a competing instrument, again due to differences ininstrument performance characteristics or detection technologies. Inaddition, the controls available from an instrument manufacturer may notcontain a particular combination of analytes and other components foundin a patient sample of interest.

Customizable quality controls are described in co assigned U.S. Pat. No.9,354,144, entitled “Customized Quality Controls for Analytical Assays”and issued on May 31, 2016, which is incorporated herein by reference.These quality controls can be prepared by dissolving one or more beads,each containing one or more analytes, in a liquid base matrix. Byselecting the number of beads and the volume of base matrix, any desiredanalyte concentration can be obtained. Other desired components can beintroduced into a control as part of the bead or matrix. With enoughbeads and base matrix, any number of controls corresponding to two,three, or more targeted analyte levels can be prepared. Customizablequality controls can thus be prepared according to the end user's needsand used to monitor any diagnostic procedure. Recoveries for thesecontrols measured with different procedures can be compared.

BRIEF SUMMARY OF THE INVENTION

Provided herein are methods of preparing a quality control for ananalyte according to a desired recovery, evaluating a predicted recoveryof a measurement procedure that can be used to measure such a qualitycontrol, and determining the relative recoveries of two or moremeasurement procedures for an analyte.

In a first aspect of the invention, a method is provided for preparing aquality control for an analyte, wherein the quality control can bemeasured using a diagnostic measurement procedure, and the nominalconcentration of the analyte in the quality control corresponds to adesired recovery of the measurement procedure for the analyte. Themethod includes: scaling the desired recovery by a correlation factor toestimate a target nominal concentration of the analyte in the qualitycontrol, wherein the correlation factor is based on data previouslyobtained using the measurement procedure; providing one or more solidbeads containing the analyte, and a base matrix; determining a number ofsolid beads and a volume of the base matrix needed to prepare thequality control with the target nominal concentration of the analyte;and dissolving the number of beads in the volume of the base matrix,thereby preparing the quality control for the analyte.

In some embodiments, the method further includes designating one or morenearby recoveries, wherein each nearby recovery is at least 10, 20, or50% above or below the desired recovery, and using interpolation,estimating nominal concentrations of the analyte corresponding to thenearby recoveries.

In some embodiments of the method, scaling the desired recovery by acorrelation factor includes converting the desired recovery to areference recovery, the reference recovery is an estimate of therecovery of a reference measurement procedure for the analyte, and thecorrelation factor is based on data previously obtained using themeasurement procedure and the reference measurement procedure. In theseembodiments, the method can also include designating one or more nearbyreference recoveries of the reference measurement procedure, whereineach nearby reference recovery is at least 10, 20, or 50% above or belowthe reference recovery, and using interpolation, estimating nominalconcentrations of the analyte corresponding to the nearby referencerecoveries.

In a second aspect of the invention, a method is provided for evaluatinga predicted recovery of a measurement procedure for an analyte, whereinthe analyte occurs in a quality control, and the predicted recoverycorresponds to the nominal concentration of the analyte in the qualitycontrol.

The method includes: determining a nominal concentration of the analytein the quality control; scaling the nominal concentration by acorrelation factor, wherein the correlation factor is based on datapreviously obtained using the measurement procedure, to obtain estimatedpredicted recovery of the measurement procedure for the analyte; testingthe quality control using the measurement procedure, thereby obtaining ameasurement response; determining an actual recovery of the measurementprocedure for the analyte based on the measurement response; andcomparing the predicted recovery with the actual recovery, therebyevaluating the estimated recovery of the measurement procedure for theanalyte.

In some embodiments of the method, the quality control is prepared byadding a number of solid beads containing the analyte to a volume of abase matrix, and the nominal concentration of the analyte in the qualitycontrol is determined based on the number of beads and the volume of thebase matrix.

In some embodiments, the method also includes designating one or morenearby concentrations of the analyte, wherein each nearby concentrationis at least 10, 20, or 50% above or below the nominal concentration ofthe analyte in the quality control, and using interpolation, obtainingrecoveries for the analyte at the nearby concentrations.

In some embodiments, the method also includes obtaining a range ofrecoveries of the measurement procedure for the analyte, wherein therange contains the predicted recovery.

In some embodiments of the method, scaling the nominal concentration bya correlation factor includes converting a reference recovery to thepredicted recovery of the measurement procedure; the reference recoveryis an estimate of the recovery of a reference measurement procedure forthe analyte at the nominal concentration of the analyte in the qualitycontrol; and the correlation factor is based on data previously obtainedusing the measurement procedure and the reference measurement procedure.

According to a third aspect of the invention, a method is provided fordetermining relative recoveries of two or more measurement procedure foran analyte, wherein the analyte occurs in a quality control. The methodincludes: providing one or more solid beads containing the analyte, anda base matrix; dissolving a number of the solid beads in a volume of thebase matrix to form a quality control; for each measurement procedure,testing the quality control, thereby obtaining a measurement response,and determining a recovery for the analyte based on the measurementresponse; and comparing the recoveries of the measurement procedure.

In some embodiments of the method, comparing the recoveries of themeasurement procedures includes calculating a ratio of the recoveries.

In some embodiments, the method also includes: forming a referencequality control, wherein the nominal concentration of the analyte in thereference quality control is different from the nominal concentration ofthe analyte in the quality control; for each measurement procedure,testing the reference quality control, thereby obtaining a referencemeasurement response, and determining a reference recovery for theanalyte based on the reference measurement response; and comparing thereference recoveries of the measurement procedures.

In embodiments of the preceding methods, the base matrix can be obtainedfrom one or more patient samples.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart illustrating embodiments of the methods describedherein.

FIG. 2 is a flowchart illustrating embodiments of the methods describedherein.

FIG. 3 is a flowchart illustrating embodiments of the methods describedherein.

FIG. 4 shows a representative value assignment table provided withnon-customizable quality controls.

FIG. 5 shows a graph of analyte nominal concentration versus number ofanalyte beads for customizable quality controls.

FIG. 6 shows a graph of analyte nominal concentration versus volume ofbase matrix for customizable quality controls.

FIGS. 7 and 8 show spreadsheets for predicting the recoveries of variousmeasurement procedures for T4 thyroid hormone. Each spreadsheet allowsthe end user to input a number of solid beads and a volume of basematrix for a customizable quality control and receive recoveries, inμg/dL and nM, for each measurement procedure as an output.

FIG. 9 shows tables of predicted recoveries of various measurementprocedures for T4 thyroid hormone in customizable quality controls.

FIG. 10 shows measured and predicted recoveries of the Beckman Accessand other diagnostic instruments for T4 thyroid hormone in customizablequality controls.

FIG. 11 shows a block diagram of a computer system 800 usable withembodiments of the present invention.

DETAILED DESCRIPTION OF THE INVENTION I. Introduction

The present methods allow the end user of a diagnostic measurementprocedure to prepare a customizable quality control for an analyte ofinterest according to a desired recovery for the analyte. Conversely,the methods allow the end user to predict the recovery of the procedurefor the analyte when the procedure is used to assay a quality controlalready prepared. The quality control can contain one analyte or manyanalytes, as well as a human bodily fluid or components thereof, and canbe used to monitor the measurement procedure for patient samples.

Each quality control is prepared by dissolving one or more solid beadscontaining an analyte of interest in a liquid base matrix. This processis described in related U.S. Pat. No. 9,354,144, referenced above, aswell as below under “Customizable Quality Controls”. The concentrationof the analyte in the quality control is estimated from the number ofbeads and the volume of base matrix. Generally, a larger number of beadsor a smaller volume of base matrix results in a higher analyteconcentration. The analyte concentration in a particular qualitycontrol, corresponding to a given number of beads and volume of basematrix, can be estimated to an absolute level (for example, in unitssuch as g/l, U/l, or micromolar), or relative to controls prepared withother numbers of beads or volumes of base matrix.

To determine the number of beads and volume of base matrix needed for adesired recovery, or conversely predict the recovery corresponding to anumber of beads and volumes of base matrix, reference is made to datapreviously obtained using the end user's chosen measurement procedure orother procedures. The data can reflect the different performancecharacteristics of various procedures, such as measurement range,precision, accuracy, limits of detection, limits of quantitation, andlinearity. Alternatively or in addition, the data can be specific to theend user's analyte of interest or related analytes. For example, thedata can include: recoveries of a procedure at various relative ornominal concentrations of the analyte; recoveries of a measurementprocedure at a fixed nominal concentration of the analyte when theanalyte is suspended in various base matrices; recoveries of multiplemeasurement procedures for the analyte when measured at the same nominalconcentration or set of bead-to-volume ratios; or recoveries of one ormore measurement procedures for multiple analytes, including the analyteof interest, when these analytes are present together in the samecontrol or sample at various relative or nominal concentrations.

The previously obtained data can be embodied in a computer program orspreadsheet, where the user can input a nominal analyte concentration(or number of beads and volume of base matrix needed to obtain thisconcentration) and receive a recovery as output, or vice versa. Bymaking reference to previously obtained data, the end user can accountfor biases between the nominal concentration of the analyte in thequality control and the recovery reported by the procedure of interest,sensitivity of the recovery to the concentrations of other components ofthe quality control, and differences in the recoveries reported bydifferent procedures. Thus, the user can establish an accuratecorrespondence between the nominal concentration of the analyte in thequality control and the expected recovery of the user's chosenmeasurement procedure for the analyte. From this correspondence, theuser can prepare a quality control for which the measurement proceduregives a desired recovery, or predict the recovery for a quality controlalready prepared.

The present methods thus provide a way to prepare a quality control foran analyte of interest, to be used in conjunction with a desiredmeasurement procedure where the analyte is present at a targeted ordiagnostically relevant level. This level can be above, near, or below amedical decision point for the analyte in a particular patientpopulation, or fall within a range considered normal or abnormal for thepopulation. Each quality control can be customized to the targetedanalyte level with high precision, and any number of quality controlscorresponding to multiple levels can be prepared. Quality controls thathave well-established or predictable recoveries at these levels can beused to monitor the procedure for testing patient samples by checkingfor consistency of the procedure over time.

The present methods are not limited to a particular kind of analyte or aparticular measurement procedure. In fact, customizable quality controlsas described herein can contain any desired analytes and be prepared ormeasured using any appropriate measurement procedures. These qualitycontrols thus afford greater flexibility than the non-customizablequality controls provided by instrument manufacturers, allowing theperformance of many instruments and measurement procedures to bemonitored.

II. Definitions

‘Sample’ refers to a biological sample obtained from one or moreorganisms, living or dead. In embodiments of the present invention, asample can be obtained from a single human subject, such as a subjectdiagnosed with a disease, a subject suspected of suffering from adisease, or a subject known or suspected to not suffer from a disease.‘Human subject’ and ‘patient’ are used interchangeably herein, and‘patient sample’ refers to a sample obtained from a patient. A samplecan be obtained from or constitute a tissue or bodily fluid. Examples ofhuman bodily fluids that can serve as samples, or from which samples canbe obtained, include blood, serum, urine, mucus, saliva, semen, vaginalfluid, synovial fluid, or cerebrospinal fluid.

‘Analyte’ refers to an ionic, molecular, or supramolecular species thatis present in a sample and can be detected. Examples of analytes includeions, gasses, small organic molecules, small inorganic molecules,proteins (e.g., enzymes or antibodies), lipids, carbohydrates, nucleicacids, membranous structures, organelles, and cells.

‘Base matrix’ refers to a material initially devoid, or largely devoid,of an analyte but to which the analyte can be added. Upon addition, theanalyte can be dissolved or suspended in the base matrix. A base matrixcan be a biological sample, such as a tissue or bodily fluid, exclusiveof the analyte or from which the analyte has been removed.Alternatively, a base matrix can contain select components of a tissueor bodily fluid. In some embodiments of the present invention, a basematrix is liquid and homogeneous.

‘Concentration’ refers to the quantity of an analyte per unit volume ofa material. The material can be a biological sample, a base matrix, aliquid, solid, gas, or any other medium. Concentration can be expressedin any desired units, for example grams per liter (g/l), moles per liter(molar, denoted by ‘M’), or activity units per liter (U/l).

‘Nominal concentration’ refers to the estimated concentration of ananalyte in a material. Nominal concentration can be determined usingaccepted methods in the diagnostic arts or as desired. For example, theconcentration of an analyte in sample can be estimated gravimetricallyor from a series of tests using an accepted reference procedure. Whenbeads containing an analyte are dissolved in a base matrix, theconcentration of the analyte in the resulting solution can be estimatedfrom the number of beads dissolved, the mass of each bead, the mass oractivity of the analyte in each bead, and/or the volume of base matrix.The concentration can also be estimated from the ratio of the number ofbeads to the volume of base matrix, termed the ‘B/MV ratio’ herein.

‘Quality control’ refers to a mixture of an analyte and a base matrix.The mixture can be used to monitor the performance of one or moremeasurement procedures, and the analyte is present in the mixture at aknown, predetermined or reproducible nominal concentration.

‘Measurement procedure’ refers to a technique or system for detectingand estimating the concentration of an analyte in a sample or qualitycontrol. A measurement procedure can encompass an instrument or manualmethod and procedures for calibration or operation.

‘Recovery’ refers to the concentration of an analyte in a sample orquality control as reported by a measurement procedure. A recovery canbe stated as a single value, such as may be obtained from a singlemeasurement. Alternatively, a recovery can be stated as a mean value,reflecting multiple measurements, and/or a range. The range canrepresent the minimum and maximum of many measurements, the mean plus orminus the variance, standard deviation, or standard error of thesemeasurements, or any other appropriate set of values reflectingstatistical variation or uncertainty.

‘Medical decision point’ refers to the concentration of an analyte in apatient sample that serves as the threshold or cutoff for a medicaldecision. The medical decision can be deciding whether the concentrationof the analyte is normal or abnormal (e.g., abnormally high orabnormally low), diagnosing the patient with a disease, or requestingthat the patient get further tests, for example. Generally, differentmedical decisions are made depending on whether the recovery of theanalyte in the patient sample is above or below the medical decisionpoint. A medical decision point can be an absolute concentration, arelative concentration, or an approximate concentration or range ofconcentrations. A medical decision point can be expressed as a recoverymeasured using a particular measurement procedure.

‘Level’ refers to the concentration or abundance of an analyte in apatient sample or quality control. A level can be stated as a singlevalue or as a range. Alternatively or in addition, a level can becharacterized by its relationship to (e.g., above, below, near,overlapping with, or representative of) another level, a medicaldecision point, or a health condition.

The terms ‘about’ and ‘approximately equal’ are used herein to modify anumerical value and indicate a defined range around that value. If “X”is the value, “about X” or “approximately equal to X” generallyindicates a value from 0.90X to 1.10X. Any reference to “about X”indicates at least the values X, 0.90X, 0.91X, 0.92X, 0.93X, 0.94X,0.95X, 0.96X, 0.97X, 0.98X, 0.99X, 1.01X, 1.02X, 1.03X, 1.04X, 1.05X,1.06X, 1.07X, 1.08X, 1.09X, and 1.10X. Thus, “about X” is intended todisclose, e.g., “0.98X.” When “about” is applied to the beginning of anumerical range, it applies to both ends of the range. Thus, “from about6 to 8.5” is equivalent to “from about 6 to about 8.5.” When “about” isapplied to the first value of a set of values, it applies to all valuesin that set. Thus, “about 7, 9, or 11%” is equivalent to “about 7%,about 9%, or about 11%.”

‘Percent above’ or ‘X % above’, where ‘X’ is a number, refers to thedifference between two numerical values. A is said to be X % above B ifA is greater than or equal to the sum of B and X % of B. The terms‘percent below’ and ‘X % below’ are used likewise. For example, A issaid to be X % below B if A is less than or equal to X % of B subtractedfrom B.

III. Customizable Quality Controls

The customizable quality controls discussed herein can be prepared bydissolving one or more solid beads, each containing one or moreanalytes, in an aqueous base matrix. These controls can be prepared bythe user at the point of use and enable the user to select theanalyte(s) that the controls contain, the number of control levels, theconcentration of analyte in each control, the base matrix, and otherfactors affecting the utility of the controls for particular assays. Theterm “bead” is used herein to denote spheres, pellets, or any solidbodies of similar size, i.e., capable of being packaged in a bottle, forexample, and used either individually or in small quantities. Inaddition to the analyte, individual beads can contain a bulking agent toadd structural integrity, plus other optional components to help controlthe characteristics and quality of the control as it is reconstituted.The base matrix is an aqueous solution of a salt and a buffer at a pH ofabout 4.0 to about 9.0, either in a human biological fluid or in water,and if in water, the solution preferably also contains human or animalsource materials that provide the matrix with the attributes of abiological sample. The beads and the base matrix can be shipped and soldseparately or as parts of a kit, and they can be combined at the site ofuse immediately prior to their use, or combined by the purchaser andstored for later use. Uncombined, the beads and matrix can be shippedand stored without special maintenance conditions such as refrigerationor freezing.

The solid beads, which are soluble in water, can conveniently bemanufactured as spheres. Provided the sizes of the spheres are uniform,these sizes are not critical to the utility or novelty of the invention,and can vary. In many cases, spheres having diameters within the rangeof from about 3 mm to about 10 mm will be convenient to use. Forcontrols to be used for single-analyte assays, each bead in certainembodiments of the invention will contain the single analyte as the onlybiologically derived species in the bead, and controls of differentnominal concentrations of that analyte can be obtained by dissolvingdifferent numbers of beads in separate and either equal or unequalvolumes of base matrix. Controls can also be formulated formulti-component assays, i.e., assays for two or more analytes, eitherfor simultaneous detection or in separate detections, by including thetwo or more analytes in the beads. Here as well, controls of differentlevels of concentration of both analytes can be obtained by dissolvingdifferent numbers of beads in different aliquots of the same volume ofthe base matrix. Controls for multi-component assays can also beprepared from beads with a single analyte per bead by combining beads ofdifferent analytes in a single volume of the base matrix, therebyallowing the user greater flexibility in the design and use of thecontrols. A set of different levels of each analyte can be achieved byusing different numbers of beads in separate but equal volumes of thebase matrix, or the same number of beads in different volumes of thebase matrix.

The quantity of analyte per control can vary widely and will be governedby the volume of the reconstituted control and the minimum number ofbeads to be used per control. For example, a given set of controls mayinclude three levels of analyte, and the quantity of analyte in a singlebead may thus be such that the control with the lowest analyte level canbe achieved by reconstituting a single bead in a volume of base matrix.The number of levels that the user will prepare in forming the controlset can also vary, and in some cases as little as two levels willsuffice. In most cases, however, controls constituting three or moreanalyte levels will be prepared, thereby allowing the user to check forlinearity of the assay response, and to have controls representinglevels approximately equal to the decision point as well as above andbelow.

In certain embodiments, the beads will contain a single analyte and noadditional species other than formulation adjuvants, which are materialsincluded to dilute the analyte in the bead or to enhance or modify thephysical characteristics of the bead and the ability of the bead todissolve or disperse rapidly in the base matrix. Formulation adjuvantsmay serve, for example, to maintain the physical integrity of the beadduring storage, shipment, or handling, to impart chemical stability tothe bead, the analyte, or both while still in bead form, to maintain theionic strength or the pH of the reconstituted control once the bead isdissolved in the base matrix, or to give the reconstituted control theattributes of a human sample in any of various respects that do notinterfere with the ability to detect the analyte. Certain beads maycontain two or more analytes, although beads that are limited to asingle analyte can offer greater flexibility in their use as they arereconstituted as controls, since controls with two or more analytes canbe prepared by combining different beads with single analytes each,allowing the laboratory technician to control or vary the relativeamounts. Optimal formulating adjuvants are those that do not interferewith the detection of the analytes in the reconstituted controls, byeither masking the analytes, being detected in combination with theanalytes in a manner that does not permit segregation of the detectionof one analyte from another, or in any way affecting the sensitivity ofthe assay toward the analyte

One type of formulation adjuvant is a bulking agent. One or more bulkingagents will provide physical integrity to the bead by helping the beadhold its shape. Conventional materials that are known to achieve thiseffect can be used. Examples of bulking agents are glycine, sorbitol,mannitol, lactose, dextrose, albumin, ovalbumin, gelatin,polysaccharides such as dextran, and hydrophilic polymers such aspolyvinylpyrrolidone. Bovine serum albumin is particularly convenient inmany cases. The appropriate volume of bulking agent will be readilyapparent to those of skill in bead formulation, and actual values arenot critical to the novelty or utility of the invention. When beads areformed by lyophilization of aqueous solutions, for example, the solutionprior to lyophilization in many cases will contain from about 0.3 g toabout 3 g of bulking agent per deciliter of solution.

Another type of formulation adjuvant is a salt, which can be included tomaintain the ionic strength of the base matrix when the beads aredissolved in the matrix. The optimal quantity of salt in the bead willthus be that amount that will produce at most a minimal differencebetween the salt concentration of the base matrix and that of thereconstituted control. Again using as examples beads that are formed bylyophilization of aqueous solutions, the salt concentration of theaqueous bead solution prior to lyophilization may range from about 10 mMto about 300 mM. The salt itself can be any salt that is compatible withbiological samples and that behaves in the same way in a control as itdoes in the sample to be assayed. Sodium chloride is a common salt forthis type of use.

A third type of formulation adjuvant is a buffer to maintain thereconstituted control at a desired pH. The pH of the bead can varywidely as evidenced by the range quoted above, but for controls fortypical assays of human and other mammalian subjects, the pH willgenerally range from neutral to slightly basic. In many cases, anoptimal pH level will be within the range of from about 6.2 to about8.5. Examples of suitable buffers are tris(hydroxymethyl)aminomethane(Tris base), tris(hydroxymethyl)aminomethane hydrochloride (Tris-HCl),bis(2-hydroxyethyl)iminotris-(hydroxymethyl)methane (Bis-Tris base),bis(2-hydroxyethyl)iminotris-(hydroxymethyl)methane hydrochloride(Bis-Tris-HCl), and N-2-hydroxyethyl-piperazine-N-2-ethanesulfonic acid(HEPES).

Beads can be prepared by any conventional means, most convenient ofwhich is by lyophilization from an aqueous solution in which thecomponents of the ultimate bead are dissolved. The volume of thesolution prior to freezing and sublimation can vary widely, although inmost cases a volume ranging from about 5 μL to about 1,000 μL willprovide the best results. Lyophilization avoids or minimizes degradationof the bead components due to exposure of the bead to elevatedtemperatures.

The human or biological source materials that are included in the basematrix in certain embodiments of this invention can be human serumalbumin, bovine serum albumin, or any other albumin or protein ingeneral that is analogous to human serum albumin. When human serumalbumin or bovine serum albumin is also included in the bead(s) as abulking agent, its concentration is preferably low enough that thedissolving of the bead in the base matrix does not result in areconstituted control with a concentration that is substantiallydifferent from that of the base matrix prior to reconstitution. Thus,when the bulking agent in the bead is bovine serum albumin (BSA) and theadditive in the base matrix is human serum albumin (HSA), theconcentration of BSA in the aqueous solution from which the bead isformed (by lyophilization, for example) may be one-tenth to one-third,for example, of the concentration of HSA in the base matrix.

The base matrix can be provided with an osmolarity that provides areconstituted control that most closely resembles the samples that areto be assayed. With these considerations in mind, osmolarity levels canvary widely, although in most cases best results will be achieved withan osmolarity within the range of from about 50 mOsm/kg to about 1,000mOsm/kg. Osmolarity can be controlled by the inclusion of a salt, as inthe beads themselves. The same types of salts can be used in both, againwith sodium chloride as a convenient example. The base matrix can alsobe provided with a buffer, conveniently using the same buffer as in thebeads.

The base matrix can also be prepared from human and animal sourcematerials that have been treated to remove endogenous analytes thatmight interfere with particular assays. Examples of these sourcematerials are human whole blood, plasma, serum, urine, and oral andsynovial fluid. Endogenous analyte removal can be achieved byfiltration, precipitation, decomposition by enzymatic and heattreatment, and chromatographic separations such as affinity separations,ion exchange, and size exclusion.

If desired, the base matrix can be obtained from one or more patientsamples. For example, two or more patient samples can be pooled toobtain a base matrix. The same sample(s) can provide a base matrix forone or more quality controls and be subjected to diagnostic tests.

Further optional components of the base matrix are stabilizers andantimicrobial agents. Examples of stabilizers are protease inhibitors,chelating agents, cryoprotectants, reducing agents, and surfactants.Examples of antimicrobial agents are sodium azide, ciprofloxacin,chloramphenicol, gentamicin, amikacin, tobramycin, and amphotericin B.Appropriate amounts of these additives will be readily apparent to thoseof skill in their use.

IV. Methods

The methods provided herein allow the end user of a diagnosticmeasurement procedure to a compute a relationship between the nominalconcentration of an analyte in a quality control and the recovery of theprocedure for the analyte when used to measure the quality control.Thus, the end user can prepare a quality control having a desiredrecovery, or predict the recovery for a quality control alreadyprepared. The quality control can be prepared by dissolving one or moresolid beads in a base matrix, as described above.

A first method is provided for preparing a quality control for ananalyte, where the quality control can be measured using an measurementprocedure, and the nominal concentration of the analyte in the qualitycontrol corresponds to a desired recovery of the measurement procedurefor the analyte. The method includes scaling the desired recovery by acorrelation factor to determine a target nominal concentration of theanalyte in the quality control The nominal concentration can beexpressed, for example, as the number of beads per unit volume of basematrix.

In this method and the other methods discussed below, any procedure thatcan be used to detect analytes in a biological sample can serve as themeasurement procedure. Commercially available instruments and systemsused for diagnostics and analyte detection include, but are not limitedto, Abbott Architect, Alfa Wasserman ACE, Beckman Coulter Unicel,Beckman Coulter Access, Biomerieux Vidas, Ortho Vitros, Roche Elecsys,Siemens Centaur, and Tosoh AIA. The procedure can also include aconventional laboratory instrument, such as a spectrophotometer, thathas been adapted or customized to detect an analyte. The measurementprocedure can detect only one analyte of interest or other analytes andspecies too, and can make use of any technologies or phenomena toperform detection. For example, detection can be based on opticalabsorbance, electrical conductivity, magnetism, radioactivity,fluorescence, chemiluminescence, electroluminescence, enzymaticactivity, antibody-antigen binding, or calorimetry. Other means ofdetection will be apparent.

The correlation factor reflects the tendency of the procedure to reportrecoveries for the analyte of interest that differ from the nominalconcentration of this analyte in a sample or quality control, when thenominal concentration can be well known. Put differently, thecorrelation factor accounts for the recovery overestimating orunderestimating the concentration of the analyte. When the nominalanalyte concentration in a quality control is expressed in differentunits from the recovery, the correlation factor can also serve totransform the recovery into units of concentration (for example, a ratioof the number of beads to the volume of base matrix, also called theB/MV ratio). The correlation factor captures the sign and degree of theexpected difference between the desired recovery and target nominalconcentration of the analyte. In some embodiments, scaling the desiredrecovery to estimate the concentration involves multiplying the desiredrecovery by a scalar quantity. For example, if a recovery of 100 μg/mLfor a certain analyte is desired, but the measurement procedure is knownto overestimate the analyte concentration in quality controls by afactor of 1.1, then the recovery can be scaled down by this factor toobtain a target nominal concentration of 100/1.1=90.9 μg/mL. Moregenerally, scaling the desired recovery by the correlation factorinvolves applying a function to the desired recovery to return thenominal concentration. The function can be a linear, quadratic, cubic,exponential, logarithmic, or power function, for example, and caninclude a constant term that is added to or subtracted from the desiredrecovery.

The correlation factor is based on data previously obtained using themeasurement procedure. The previous data can be obtained on the sameinstrument or multiple instruments, and can reflect variability ofrecovery over time or among instruments. The data can be solicited frommultiple labs (for example, by sending each lab the same sample,standard, or quality control) or be obtained through collaborationsbetween labs (for example, through the Bio-Rad UNITY InterlaboratoryProgram). Regression analysis can be applied to the previous data toobtain a correlation factor appropriate for a desired analyte recoveryor nominal analyte concentration. For example, a regression line can befitted to a plot of analyte recovery versus nominal analyteconcentration, and the correlation factor can be obtained from the fitfunction, or by reading values from the regression line. Otherregression methods appropriate for the numerical relationship betweenanalyte recovery and nominal analyte concentration can be employed.

In some embodiments, the previous data are obtained from end users ofthe measurement procedure testing quality controls, which are preparedas described herein by dissolving beads containing the analyte in a basematrix. Thus, correlations between analyte recovery and nominal analyteconcentration are established empirically and can be updated over time(FIG. 1). The previous data can correspond to quality controls preparedfrom a particular lot or batch of beads, for example beads belonging tothe same lot as those being used by the end user to carry out thepresent method, or to the most recently manufactured lot of beads.Similarly, the previous data can correspond to quality controls preparedfrom a particular lot, batch, or kind of base matrix.

In some embodiments, the previous data on which the correlation factoris based are obtained by testing non-customizable quality controls (FIG.2). These quality controls can have fixed nominal analyte concentrationsand can be provided pre-prepared to end users of the measurementprocedure. The previous data can be obtained from end users of themeasurement procedure. It will be recognized that, to calculate anaccurate correlation factor from these data, the base matrix used in thenon-customizable quality controls should be similar in composition tothat with which the quality control in the present method will beprepared.

The end user of the analysis program can apply the correlation factor byentering the desired recovery into a computer program or spreadsheet.The computer program or spreadsheet can also house data previouslyobtained using the measurement procedure, and calculate the correlationfactor based on the input information and other data. The user can thenreceive, as an output from the program, a nominal concentration of theanalyte corresponding to the desired recovery.

Once a target nominal concentration of the analyte in the qualitycontrol has been determined (expressed, for example, as a B/MV ratio),the quality control can be prepared using one or more solid beadscontaining the analyte, and a base matrix. Specifically, a number ofsolid beads is dissolved into a volume of the base matrix, as describedabove (“Customizable Quality Controls”), so that the analyte is presentin the resulting solution (i.e., the quality control) at the targetnominal concentration. Any of the various embodiments of the solid beadsand base matrix described above, and combinations thereof, can beprovided for this purpose. For example, the solid beads can containbulking agents, and the base matrix can be an aqueous solutioncontaining components of a human bodily fluid.

In some embodiments, the solid beads and base matrix are supplied alongwith software used to compute the correlation factor and determine thetarget nominal concentration of the analyte. The software can then beused to determine a number of solid beads and a volume of base matrixconsistent with the target nominal concentration. For example,information about a particular lot of solid beads and/or base matrix,such as the amount (mass or mole quantity) of analyte per bead, thevariability in this amount, or the nominal concentrations of adjuvantsin the beads and base matrix, can be incorporated into the software, sothat a number of solid beads and a volume of base matrix are returned tothe end user along with the target nominal concentration. Alternatively,the software can let the end user provide this information as one ormore inputs. If desired, the software can take into account the volumedisplaced by the solid beads, and accordingly correct the recommendednumber of solid beads and volume of base matrix needed to prepare thequality control with a desired recovery. The end user can also calculatea number of solid beads and a volume of base matrix needed for thequality control by hand, with knowledge of the amount of analyte perbead and the target nominal concentration of the analyte. It will berecognized that different numbers of beads and volumes of base matrixcan be used to prepare the quality control, because the analyteconcentration in the quality control is determined by the ratio of thesequantities.

Some embodiments of the method also include designating one or morenearby recoveries, each at least 10, 20, or 50% above or below thedesired recovery, and estimating concentrations of the analytecorresponding to the nearby recoveries. These steps can be used toprepare additional quality controls representing different levels ofanalyte. For example, if the desired recovery corresponds to anintermediate level of the analyte, or a normal level in a particularpatient population, then the nearby recoveries can correspond to low,high, abnormally low, or abnormally high levels of the analyte. Thus,additional quality controls corresponding to the nearby recoveries canbe used to identify abnormal or disparate analyte concentrations inpatient samples.

When one or more nearby recoveries are designated, the correspondinganalyte concentrations are estimated in the same manner as that for thedesired recovery. That is, each nearby recovery is scaled by acorrelation factor according to data previously obtained using themeasurement procedure. The nearby recoveries can fall in differentportions of the measurement range of the measurement procedure from thedesired recovery and from each other. Thus, the correlation factors canbe obtained by interpolating between points in the previously obtaineddata.

In some embodiments, scaling the desired recovery by a correlationfactor includes converting the desired recovery to a reference recovery.The reference recovery is an estimate of the recovery of a referencemeasurement procedure (optionally designated ‘Procedure B’) for theanalyte. The correlation factor is based on data previously obtainedusing the reference procedure, i.e., Procedure B, in addition to datafor the end user's chosen procedure. The correlation factor can thusreflect how recoveries of the two measurement procedures compare undercomparable conditions. In any embodiment of the methods discussedherein, a measurement procedure may designated as a reference procedurefor any reason, including acceptance by experts, ease of use, number ofusers, precision, or accuracy.

As appropriate, data previously obtained for any two procedures can becompared or contrasted to calculate the correlation factor, with eitherprocedure serving as the reference procedure. If desired, data fromadditional reference procedures (e.g., Procedures C or D) can beincluded in the calculation (FIG. 3). The calculation can includeperforming regression analysis to establish numerical relationshipsamong the recoveries reported by the two or more measurement procedures.For example, the recoveries reported by two measurement procedures canbe displayed on a plot such that recoveries reported by the firstmeasurement procedure are plotted on the x-axis and recoveries reportedby the second measurement procedure are plotted on the y-axis. Thus,each point on the plot represents testing a nearly identical qualitycontrol with the two procedures, and the different points can representdifferent nominal analyte concentrations. A regression line can then befitted to the plot to provide the recovery of one procedure as afunction of recovery of the other procedure. The data used to calculatethe correlation factor can be obtained as desired, for example by themanufacturer of the beads and base matrix used to prepare customizablequality controls, or by end users of the measurement procedures. Inembodiments where the correlation factor is based on data previouslyobtained using more than one measurement procedure, one or more nearbyreference recoveries can be designated, in analogy to the nearbyrecoveries discussed above.

The quality controls discussed herein need not be prepared in view ofdesired recoveries for the analytes they contain. To the contrary,quality controls can be prepared and measured using an measurementprocedure without targeting specific recoveries beforehand. This can bedone, for example, when the measurement range of the measurementprocedure is being tested for linearity, or when the nominalconcentrations of an analyte in a series of quality controls are set byconvenience (e.g., 1, 2, or 3 beads dissolved in the same volume of basematrix). However, it can also be useful to estimate the recovery of ameasurement procedure to quantify the relationship between the nominalconcentration and recovery of the analyte.

A second method is provided herein for evaluating a predicted recoveryof an measurement procedure for an analyte, where the analyte occurs ina quality control or linearity set, and the estimated recoverycorresponds to the nominal concentration of the analyte. The methodincludes determining a nominal concentration of the analyte in thequality control. The nominal concentration can be in any units, and canbe absolute or relative to another quality control or reference. Inembodiments where the quality control is prepared by adding a number ofsolid beads containing the analyte to a volume of a base matrix, thenominal concentration of the analyte in the quality control can bedetermined based on this number. For example, the nominal concentrationcan be determined by dividing the total amount of analyte (calculated asthe amount of analyte per bead times the number of beads) by the volumeof base matrix. Alternatively, the nominal concentration can beexpressed simply as the B/MV ratio. If desired, the nominalconcentration can be corrected for the excluded volume of the beads.

Once determined, the nominal concentration is scaled by a correlationfactor, which is based on data previously obtained using the measurementprocedure, to obtain an predicted recovery of the measurement procedurefor the analyte. The correlation factor is similar to the correlationfactor discussed above for the first method, but provides analyterecovery as a function of nominal concentration, rather than nominalconcentration as a function as recovery. In cases where the correlationfactor is a single scalar quantity, the correlation factor for thepresent second method can be the reciprocal of the correlation factorfor the first method. For example, if previous data indicate that ameasurement procedure reports a recovery in excess of the nominalconcentration of an analyte by a factor of 1.1, then the nominalconcentration can be multiplied by a correlation factor of 1.1 to obtainthe predicted recovery. The present correlation factor generallyreflects the tendency of the recovery to overestimate or underestimatethe approximate analyte concentration in the quality control, or anyother systematic disagreement between these variables. The correlationfactor can also reflect any difference in units between the recovery andnominal concentration.

The correlation factor can be calculated as desired based on datapreviously obtained using the measurement procedure. For example, thecorrelation factor can reflect varying performance of the procedure indifferent parts of its measurement range, and sensitivity of theprocedure to the nominal concentrations of other analytes or solutes.The correlation factor can be general to test methodologies that can beperformed on many instruments, or specific to a methodology as performedon a particular instrument. Other considerations for calculating thecorrelation factor will be apparent in view of the discussion above. Forexample, regression analysis can be performed on recovery datapreviously obtained at many nominal analyte concentrations, in order tocalculate a correlation factor at the particular nominal analyteconcentration in the end user's quality control. As for the firstmethod, the correlation factor can be calculated in software byinputting this nominal analyte concentration, or the number of beads andvolume of base matrix used to prepare the quality control.

Evaluating the predicted analyte recovery for the quality controlfurther includes determining an actual recovery. First the qualitycontrol is tested using the measurement procedure to obtain aninstrument measurement response. Next, an actual recovery of themeasurement procedure for the analyte is determined based on themeasurement response. The measurement can be made as appropriate for themeasurement procedure or instrumentation associated with the procedure,and can involve blanking or other preliminary steps. The actual recoverycan be determined automatically from the measurement, for example as aread-out from an instrument, or can be calculated by the end user usingan observable from the measurement. Observables can be binary (thepresence or absence of a signal), qualitative, or quantitative. Examplesof observables include but are not limited to optical absorbance,fluorescent emission, enzymatic activity, and radioactivity.

To complete the evaluation of the predicted recovery of the measurementprocedure for the analyte, the predicted recovery is compared with theactual recovery. Comparing these recoveries can include calculating aratio or difference, or simply noting which recovery is larger orsmaller. The comparison can be made in software or by the end user. Theresults of the comparison can be used as desired by the end user orothers, for example to monitor the measurement procedure or prepareadditional quality controls.

Some embodiments of the present method also include designating one ormore nearby concentrations of the analyte, for example each at least 10,20, or 50% above or below the nominal concentration of the analyte inthe quality control, and obtaining predicted recoveries for the analyteat the nearby concentrations. The nearby concentrations can correspondto quality controls that are prepared in addition to that for which thenominal analyte concentration is determined and the recovery ispredicted. Alternatively, the nearby concentrations can be designatedsimply by scaling, adding to, or subtracting from the determinedconcentration and need not correspond to actual quality controls.Recoveries for the analyte at the nearby concentrations can be predictedby scaling each nearby concentration by a correlation factor asdiscussed above. The correlation factors can be obtained byinterpolating between points in previously obtained data.

In some embodiments, when an analyte's nominal concentration in aquality control is known and is scaled by a correlation factor, a rangeof recoveries is obtained in addition to a single value. The range cancontain the predicted recovery and reflects the practice followed bymany laboratories of reporting a range of recoveries for each analytelevel or quality control. In these embodiments, the predicted recoverycan be a mean of the expected recoveries, and the range can be a measureof spread or uncertainty in the expected recoveries, such as standarddeviation.

In some embodiments, scaling the nominal concentration by a correlationfactor includes converting a reference recovery to the predictedrecovery of the measurement procedure. Here, the reference recovery isan estimate of the recovery of a reference measurement procedure(“Procedure B”) for the analyte at the nominal concentration of theanalyte in the quality control. Thus, the recovery is predicted in atwo-step process, where the analyte's nominal concentration is firstlikened to a reference recovery (i.e., the recovery of Procedure B) andthe reference recovery is then converted to a predicted recovery for theend user's measurement procedure of interest. The correlation factor canbe based on data previously obtained using both measurement procedures,as discussed above, and can capture how recoveries reported by the twoprocedures compare under comparable conditions. The correlation factorcan include or be calculated using any function (e.g., linear orquadratic) that relates the reference recovery to the predictedrecovery. More than one measurement procedure can serve as a referencemeasurement procedure to relate the recoveries of various measurementprocedures to each other. It will be recognized that accuratecorrelation factors can be more easily calculated if the measurementprocedure and reference measurement procedure(s) have similarmeasurement ranges.

In order to determine correlation factors, and for any other desiredpurposes, a third method is provided herein for determining relativerecoveries of two or more measurement procedures for an analyte, wherethe analyte occurs in a quality control. The method includes preparing aquality control using solid beads and a base matrix. First, one or moresolid beads containing the analyte, and a base matrix, are provided.Next, a number of the solid beads is dissolved in a volume of the basematrix to form the quality control. These steps are described in greaterdetail above. The solid beads and base matrix can have any of theattributes discussed above, and the quality control can contain anyanalyte or analytes.

Once the quality control is prepared, it is used to determine relativerecoveries of the two or more measurement procedures. For eachmeasurement procedure, the quality control is tested, and a recovery forthe analyte is determined based on the instrument response. Therecoveries are then compared. The recoveries can be determined asappropriate for each measurement procedure, and can be compared bycalculating a ratio or difference, or otherwise.

Embodiments of this method can also include forming a reference qualitycontrol having a nominal analyte concentration different from that ofthe original quality control. Recoveries of the two or more proceduresare also determined for the reference quality control and are termedreference recoveries. The reference recoveries can be compared with eachother or with the recoveries determined from the original qualitycontrol. Using both quality controls, the sensitivities of themeasurement procedures to the analyte of interest can be examined atmultiple nominal concentrations of the analyte. Thus, systematicagreement or disagreement of the recoveries reported by multiplemeasurement procedures can be identified. To this end, if desired,additional reference quality controls can be prepared and recoveries canbe determined at many nominal concentrations.

V. EXAMPLES A. Expected Values Using Tables or Graphs Supplied by theControl Manufacturer

Presented in FIG. 4 is a typical value assignment table for threenon-customizable quality controls. The quality controls, labeled “Level1”, “Level 2”, and “Level 3”, contain different nominal concentrationsof the D-dimer analyte. Each row of the table provides an expectedrecovery, expressed as a mean and a range, of a particular measurementprocedure for each quality control.

Presented in FIGS. 5 and 6 are graphs that the end user of a diagnosticprocedure can use to prepare customized quality controls having desirednominal concentrations of the analyte, when the amount of analyte persolid bead is known. FIG. 5 shows the nominal analyte concentrations ina series of customized quality controls prepared by dissolving differentnumbers of beads in a fixed volume of base matrix. The nominalconcentrations have been determined empirically by the manufacturer ofthe beads and base matrix. The graph covers a broad range of nominalconcentrations and preferably covers the reportable range of mostmeasurement procedures. The end user can determine the expected nominalconcentration in a particular quality control by reading a number fromthe graph or interpolating along the graph. For example, if nine beadseach containing 50 ng of analyte are dissolved in 5 mL of base matrix,the nominal concentration of analyte in the resulting quality control isexpected to be 90 ng/mL. FIG. 6 similarly shows the nominal analyteconcentrations in another series of customized quality controls preparedby dissolving one 50 ng bead in different volumes of base matrix. If thebead is rehydrated in 3.5 mL of the base matrix, for example, thenominal concentration of analyte in the resulting quality control isexpected to be 14 ng/mL. This nominal concentration can be read from thegraph.

B. Example 2. Expected Recoveries Determined Using Spreadsheets

FIGS. 7 and 8 demonstrate an approach to estimating recoveries ofvarious measurement procedures for T4 thyroid hormone.

Three reference customizable quality controls, containing low, medium,and high levels of T4 hormone, were prepared as described above. Eachreference quality control was tested with seven different measurementprocedures (Abbott Architect, Alfa Wasserman ACE, Beckman CoulterUnicel, Biomerieux Vidas, Ortho Vitros, Roche Elecsys, and Siemens AdviaCentaur) and recoveries for each procedure were recorded. For eachprocedure besides Abbot Architect, the recoveries were plotted versusthose obtained from the Abbott Architect procedure and linear regressionwas performed. The resulting linear fit function took a recovery fromthe Abbott Architect procedure at a given nominal T4 hormoneconcentration as the X value and returned a recovery for the othermeasurement procedure as the Y value. The fit function for eachprocedure had the form Recovery (procedure)=slope*Recovery (AbbottArchitect)+intercept.

The fit functions for the different measurement procedures were used inthe spreadsheet shown in FIGS. 7 and 8. This spreadsheet allows the enduser to enter the number of solid beads and volume of base matrix usedto prepare a new customizable quality control, and receive predictedrecoveries of the seven measurement procedures as outputs. The recoveryof the Abbott Architect procedure is determined by scaling the nominalconcentration of T4 hormone in the quality control, which is expressedas the ratio of number of beads to volume of base matrix, by acorrelation factor. The correlation factor is a scalar value based onhistorical data, and the scaling is done by multiplying the nominalconcentration by the correlation factor. The recovery of each of theother measurement procedures is determined using the recovery of theAbbott Architect procedure and the appropriate fit function calculatedabove.

In FIG. 7, predicted recoveries of the seven measurement procedures areshown for a customizable quality control prepared by dissolving twobeads in 5 mL of base matrix. In FIG. 8, expected recoveries of theseven procedures are shown for a customizable quality control preparedby dissolving two beads in 10 mL of base matrix.

The T4 hormone recoveries that are output by the spreadsheet shown inFIGS. 7 and 8 can be tabulated as shown in FIG. 9. Each table in FIG. 9shows the recoveries of one procedure calculated for variouscombinations of bead number and volume of base matrix. Each table alsoshows how the recoveries are positioned with respect to two medicaldecision points for distinguishing among hypothyroid, euthyroid, andhyperthyroid levels of T4 hormone. These medical decision pointscorrespond to recoveries of approximately 4 and 10 μg/dL as measuredusing the Abbott Architect procedure.

C. Example 3. Value Assignment Example of Bead Based Control Using UNITYData

Customizable quality controls prepared from various numbers of T4analyte beads and volumes of base matrix were measured on a BeckmanAccess instrument and recoveries were recorded (FIG. 10, top). Therecoveries were then plotted versus the ratio of bead number to volumeof base matrix (B/MV ratio). This ratio served as the nominalconcentration of T4 hormone. Using least-squares regression (FIG. 10,middle), analyte recovery on the Beckman Access instrument was estimatedas a function of this ratio using the equationRecovery=10.8*Ratio+0.0058. The function served as a correlation factorfor scaling analyte nominal concentrations to obtain recoveries.

Next, using historical data from the Bio-Rad UNITY InterlaboratoryProgram for similar controls, recoveries of the Beckman Accessinstrument and other instruments were estimated at three levels of T4hormone (FIG. 10, bottom). The recoveries of each other instrument wereplotted against those of the Beckman Access instrument and a regressionline was fitted to the data. The fit function converted the recovery ofthe Beckman Access instrument to that of the other instrument. Forexample, the recovery of the Beckman Access instrument was converted tothe recovery of the TOSOH AIA instrument by the equation Recovery (TOSOHAIA)=0.91*Recovery (Beckman Access)+0.74.

Using the data in FIG. 7 taken together, and by combining the aboveequations, the recovery of any measurement procedure for T4 thyroidhormone can be estimated based on the number of beads and volume of basematrix in a customizable quality control. A spreadsheet can be providedto the end user to input the number of beads and volume of base matrixand predict recoveries on one or more instruments of choice.

VI. Computer Systems

Any of the computer systems mentioned herein may utilize any suitablenumber of subsystems. Examples of such subsystems are shown in FIG. 11in computer apparatus 1100. In some embodiments, a computer systemincludes a single computer apparatus, where the subsystems can be thecomponents of the computer apparatus. In other embodiments, a computersystem can include multiple computer apparatuses, each being asubsystem, with internal components.

The subsystems shown in FIG. 11 are interconnected via a system bus1175. Additional subsystems such as a printer 1174, keyboard 1178,storage device(s) 1179, monitor 1176, which is coupled to displayadapter 1182, and others are shown. Peripherals and input/output (I/O)devices, which couple to I/O controller 1171, can be connected to thecomputer system by any number of means known in the art, such as serialport 1177. For example, serial port 1177 or external interface 1181(e.g. Ethernet, Wi-Fi, etc.) can be used to connect computer system 1100to a wide area network such as the Internet, a mouse input device, or ascanner. The interconnection via system bus 1175 allows the centralprocessor 1173 to communicate with each subsystem and to control theexecution of instructions from system memory 1172 or the storagedevice(s) 1179 (e.g., a fixed disk, such as a hard drive or opticaldisk), as well as the exchange of information between subsystems. Thesystem memory 1172 and/or the storage device(s) 1179 may embody acomputer readable medium. Any of the data mentioned herein can be outputfrom one component to another component and can be output to the user.

A computer system can include a plurality of the same components orsubsystems, e.g., connected together by external interface 1181 or by aninternal interface. In some embodiments, computer systems, subsystem, orapparatuses can communicate over a network. In such instances, onecomputer can be considered a client and another computer a server, whereeach can be part of a same computer system. A client and a server caneach include multiple systems, subsystems, or components.

It should be understood that any of the embodiments of the presentinvention can be implemented in the form of control logic using hardware(e.g. an application specific integrated circuit or field programmablegate array) and/or using computer software with a generally programmableprocessor in a modular or integrated manner. As user herein, a processorincludes a multi-core processor on a same integrated chip, or multipleprocessing units on a single circuit board or networked. Based on thedisclosure and teachings provided herein, a person of ordinary skill inthe art will know and appreciate other ways and/or methods to implementembodiments of the present invention using hardware and a combination ofhardware and software.

Any of the software components or functions described in thisapplication may be implemented as software code to be executed by aprocessor using any suitable computer language such as, for example,Java, C++ or Perl using, for example, conventional or object-orientedprocedures. The software code may be stored as a series of instructionsor commands on a computer readable medium for storage and/ortransmission, suitable media include random access memory (RAM), a readonly memory (ROM), a magnetic medium such as a hard-drive or a floppydisk, or an optical medium such as a compact disk (CD) or DVD (digitalversatile disk), flash memory, and the like. The computer readablemedium may be any combination of such storage or transmission devices.

Such programs may also be encoded and transmitted using carrier signalsadapted for transmission via wired, optical, and/or wireless networksconforming to a variety of protocols, including the Internet. As such, acomputer readable medium according to an embodiment of the presentinvention may be created using a data signal encoded with such programs.Computer readable media encoded with the program code may be packagedwith a compatible device or provided separately from other devices(e.g., via Internet download). Any such computer readable medium mayreside on or within a single computer product (e.g. a hard drive, a CD,or an entire computer system), and may be present on or within differentcomputer products within a system or network. A computer system mayinclude a monitor, printer, or other suitable display for providing anyof the results mentioned herein to a user.

Any of the methods described herein may be totally or partiallyperformed with a computer system including one or more processors, whichcan be configured to perform the steps. Thus, embodiments can bedirected to computer systems configured to perform the steps of any ofthe methods described herein, potentially with different componentsperforming a respective steps or a respective group of steps. Althoughpresented as numbered steps, steps of methods herein can be performed ata same time or in a different order. Additionally, portions of thesesteps may be used with portions of other steps from other methods. Also,all or portions of a step may be optional. Additionally, any of thesteps of any of the methods can be performed with modules, circuits, orother means for performing these steps.

All documents (for example, patents, patent applications, books, journalarticles, or other publications) cited herein are incorporated byreference in their entirety and for all purposes, to the same extent asif each individual document was specifically and individually indicatedto be incorporated by reference in its entirety for all purposes. To theextent such documents incorporated by reference contradict thedisclosure contained in the specification, the specification is intendedto supersede and/or take precedence over any contradictory material.

Many modifications and variations of this invention can be made withoutdeparting from its spirit and scope, as will be apparent to thoseskilled in the art. The specific embodiments described herein areoffered by way of example only and are not meant to be limiting in anyway. It is intended that the specification and examples be considered asexemplary only, with the true scope and spirit of the invention beingindicated by the following claims.

What is claimed is:
 1. A method of determining a correlation factor ofan analyte in a quality control using two or more measurement proceduresfor the analyte, the method comprising: providing one or more solidbeads containing the analyte, and a base matrix; dissolving a number ofthe solid beads in a volume of the base matrix to form a qualitycontrol; for each measurement procedure: testing the quality control,thereby obtaining a measurement response for the analyte, anddetermining a reported concentration for the analyte based on themeasurement response; comparing the reported concentrations for theanalyte determined by each of the measurement procedures to calculatethe correlation factor; determining a reported concentration for theanalyte in a new quality control using a first of the two or moremeasurement procedures; and predicting a reported concentration of theanalyte in the new quality control using a second of the two or moremeasurement procedures, based on the correlation factor.
 2. The methodof claim 1, wherein comparing the reported concentrations of themeasurement procedures comprises calculating a ratio of the reportedconcentrations.
 3. The method of claim 1, further comprising: forming areference quality control, wherein a concentration of the analyte in thereference quality control is different from a concentration of theanalyte in the quality control; for each measurement procedure: testingthe reference quality control, thereby obtaining a reference measurementresponse for the analyte, and determining a reference reportedconcentration for the analyte based on the reference measurementresponse; and comparing the reference reported concentrations of theanalyte using each of the measurement procedures to identify asystematic agreement or disagreement among the two or more measurementprocedures.
 4. The method of claim 3, wherein comparing the reportedconcentrations of the measurement procedures comprises performingregression analysis to establish a function between the reportedconcentrations of two or more measurement procedures.
 5. The method ofclaim 4, wherein performing regression analysis comprises fitting aregression line to a plot of the reported concentrations using one ofthe two or more measurement procedures versus the reportedconcentrations using another of the two or more measurement procedures.6. The method of claim 3, further comprising: testing sensitivities ofthe two or more measurement procedures to the analyte at theconcentration of the analyte in the reference quality control and theconcentration of the analyte in the quality control.
 7. The method ofclaim 6, further comprising: identifying analyte concentrations forwhich the reported concentrations of the two or more measurementsprocedures are in agreement or disagreement with one another.
 8. Themethod of claim 3, further comprising: forming one or more additionalreference quality controls, wherein the concentration of the analyte ineach of the one or more additional reference quality controls isdifferent from the concentration of the analyte in the quality controland in each other reference quality control; for each measurementprocedure and additional reference quality control: testing theadditional reference quality control, thereby obtaining an additionalreference measurement response for the analyte, and determining anadditional reference reported concentration for the analyte based on theadditional reference measurement response; and comparing the additionalreference reported concentrations of the analyte using each of themeasurement procedures.
 9. The method of claim 1, wherein the comparingcomprises calculating a ratio of (1) the reported concentration for theanalyte in the quality control using the second measurement procedure to(2) the reported concentration for the analyte in the quality controlusing the first measurement procedure, and wherein the predictingcomprises multiplying the ratio by the reported concentration for theanalyte in the new quality control using the first measurementprocedure.
 10. The method of claim 1, wherein the comparing comprisesperforming regression analysis to establish a function of the reportedconcentration using the second measurement procedure in terms of thereported concentration using the first measurement procedure, andwherein the predicting comprises applying the function to the reportedconcentration for the analyte in the new quality control using the firstmeasurement procedure.
 11. A method for predicting a reportedconcentration of an analyte in a quality control using a measurementprocedure, the method comprising: preparing two or more referencequality controls, wherein each reference quality control has a differentconcentration of the analyte, and wherein for each reference qualitycontrol the preparing comprises: providing a base matrix and one or moresolid beads containing the analyte; and dissolving a number of the solidbeads in a volume of the base matrix to form the reference qualitycontrol; for each of the measurement procedure and a referencemeasurement procedure: testing each of the two or more reference qualitycontrols, thereby obtaining measurement responses for the analyte; anddetermining reported concentrations for the analyte based on themeasurement responses; comparing for each of the two or more referencequality controls the reported concentration using the measurementprocedure and the reported concentration using the reference measurementprocedure, thereby establishing a numerical relationship; preparing anew quality control by dissolving a number of the solid beads in avolume of the base matrix; determining a reported concentration for theanalyte in the new quality control using the reference measurementprocedure; and applying the numerical relationship to the reportedconcentration for the analyte in the new quality control using thereference measurement procedure, thereby predicting the reportedconcentration for the analyte in the new quality control using themeasurement procedure.
 12. The method of claim 11, wherein the numericalrelationship is a ratio of (1) the reported concentration for theanalyte in each of the two or more reference quality controls using themeasurement procedure to (2) the reported concentration for the analytein each of the two or more reference quality controls using thereference measurement procedure, and wherein the applying comprisesmultiplying the ratio by the reported concentration for the analyte inthe new quality control using the reference measurement procedure. 13.The method of claim 11, wherein the comparing comprises performingregression analysis, and wherein the numerical relationship comprises afunction of the reported concentration using the measurement procedurein terms of the reported concentration using the reference measurementprocedure.
 14. The method of claim 13, wherein the regression analysiscomprises fitting a regression line to a plot of the reportedconcentrations using the measurement procedure versus the reportedconcentrations using the reference measurement procedure.